pizza restaurants analysis
Sowmya Chavali
02/04/2021
Pizza! It’s one of America’s most beloved dishes, eaten anywhere, anytime, and by everyone. The objective of this project is to find excellent pizza based on the location, review and price, and finally build a pizza-map focused on New York, which is known as the “Pizza Capital of the U.S.”
I am using the data sources from Jared, Barstool, and Datafiniti. Jared’s data is from top NY pizza restaurants, with a 6-point Likert scale survey on ratings. The Barstool sports dataset has critic, public, and the Barstool Staff’s rating as well as pricing, location, and geo-location. Datafiniti includes 10000 pizza places, their price ranges and geo-locations.
library(readr)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(ggmap)
library(car)
library(plotly)
library(bootstrap)
library(cluster)
library(factoextra)
library(gridExtra)
library(leaflet)
library(DataExplorer)Importing Data
barstool <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_barstool.csv")
datafiniti <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_datafiniti.csv")
jared <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_jared.csv")The purpose of the data is to find the best pizza restaurant, focusing New York City. To achieve that Tyler Ricards recorded the web traffic coming through the OneBite application.
Source data breakdown
dim(barstool)## [1] 463 22dim(datafiniti)## [1] 10000 10dim(jared)## [1] 375 9Identifying missing values
colSums(is.na(barstool)) # 2 missing observations - latitude and longitude## name address1
## 0 0
## city zip
## 0 0
## country latitude
## 0 2
## longitude price_level
## 2 0
## provider_rating provider_review_count
## 0 0
## review_stats_all_average_score review_stats_all_count
## 0 0
## review_stats_all_total_score review_stats_community_average_score
## 0 0
## review_stats_community_count review_stats_community_total_score
## 0 0
## review_stats_critic_average_score review_stats_critic_count
## 0 0
## review_stats_critic_total_score review_stats_dave_average_score
## 0 0
## review_stats_dave_count review_stats_dave_total_score
## 0 0unique_datafiniti <- datafiniti %>% distinct()
colSums(is.na(unique_datafiniti)) # No missing observations## name address city country province
## 0 0 0 0 0
## latitude longitude categories price_range_min price_range_max
## 0 0 0 0 0colSums(is.na(jared)) # 5 missing observations - percent## polla_qid answer votes pollq_id question place
## 0 0 0 0 0 0
## time total_votes percent
## 0 0 5No of Zero values in each column
colSums(barstool == 0)## name address1
## 0 0
## city zip
## 0 0
## country latitude
## 0 NA
## longitude price_level
## NA 21
## provider_rating provider_review_count
## 0 0
## review_stats_all_average_score review_stats_all_count
## 0 0
## review_stats_all_total_score review_stats_community_average_score
## 0 41
## review_stats_community_count review_stats_community_total_score
## 41 41
## review_stats_critic_average_score review_stats_critic_count
## 401 401
## review_stats_critic_total_score review_stats_dave_average_score
## 401 0
## review_stats_dave_count review_stats_dave_total_score
## 0 0colSums(unique_datafiniti == 0)## name address city country province
## 0 0 0 0 0
## latitude longitude categories price_range_min price_range_max
## 0 0 0 1852 0colSums(jared == 0)## polla_qid answer votes pollq_id question place
## 0 0 104 0 0 0
## time total_votes percent
## 0 5 NASummary of each dataset
summary(barstool)## name address1 city zip
## Length:463 Length:463 Length:463 Min. : 1748
## Class :character Class :character Class :character 1st Qu.:10009
## Mode :character Mode :character Mode :character Median :10019
## Mean :18531
## 3rd Qu.:11234
## Max. :94133
##
## country latitude longitude price_level
## Length:463 Min. :25.79 Min. :-122.41 Min. :0.00
## Class :character 1st Qu.:40.72 1st Qu.: -74.09 1st Qu.:1.00
## Mode :character Median :40.75 Median : -73.99 Median :1.00
## Mean :40.19 Mean : -77.44 Mean :1.46
## 3rd Qu.:40.78 3rd Qu.: -73.97 3rd Qu.:2.00
## Max. :45.00 Max. : -70.09 Max. :3.00
## NA's :2 NA's :2
## provider_rating provider_review_count review_stats_all_average_score
## Min. :2.000 Min. : 2.0 Min. :0.100
## 1st Qu.:3.500 1st Qu.: 74.0 1st Qu.:6.240
## Median :3.500 Median : 169.0 Median :7.162
## Mean :3.671 Mean : 386.1 Mean :6.876
## 3rd Qu.:4.000 3rd Qu.: 392.0 3rd Qu.:7.809
## Max. :5.000 Max. :5797.0 Max. :9.079
##
## review_stats_all_count review_stats_all_total_score
## Min. : 1.00 Min. : 0.10
## 1st Qu.: 4.00 1st Qu.: 23.65
## Median : 8.00 Median : 54.10
## Mean : 19.02 Mean : 149.93
## 3rd Qu.: 19.00 3rd Qu.: 140.20
## Max. :568.00 Max. :5045.60
##
## review_stats_community_average_score review_stats_community_count
## Min. : 0.000 Min. : 0.00
## 1st Qu.: 6.075 1st Qu.: 3.00
## Median : 7.225 Median : 7.00
## Mean : 6.457 Mean : 17.87
## 3rd Qu.: 7.873 3rd Qu.: 18.00
## Max. :10.000 Max. :567.00
##
## review_stats_community_total_score review_stats_critic_average_score
## Min. : 0.00 Min. : 0.0000
## 1st Qu.: 15.65 1st Qu.: 0.0000
## Median : 47.30 Median : 0.0000
## Mean : 142.28 Mean : 0.9717
## 3rd Qu.: 135.10 3rd Qu.: 0.0000
## Max. :5036.30 Max. :11.0000
##
## review_stats_critic_count review_stats_critic_total_score
## Min. :0.0000 Min. : 0.000
## 1st Qu.:0.0000 1st Qu.: 0.000
## Median :0.0000 Median : 0.000
## Mean :0.1425 Mean : 1.023
## 3rd Qu.:0.0000 3rd Qu.: 0.000
## Max. :5.0000 Max. :29.800
##
## review_stats_dave_average_score review_stats_dave_count
## Min. : 0.080 Min. :1
## 1st Qu.: 6.200 1st Qu.:1
## Median : 7.100 Median :1
## Mean : 6.623 Mean :1
## 3rd Qu.: 7.800 3rd Qu.:1
## Max. :10.000 Max. :1
##
## review_stats_dave_total_score
## Min. : 0.080
## 1st Qu.: 6.200
## Median : 7.100
## Mean : 6.623
## 3rd Qu.: 7.800
## Max. :10.000
## summary(datafiniti)## name address city country
## Length:10000 Length:10000 Length:10000 Length:10000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## province latitude longitude categories
## Length:10000 Min. :21.42 Min. :-157.80 Length:10000
## Class :character 1st Qu.:34.42 1st Qu.:-104.80 Class :character
## Mode :character Median :40.12 Median : -82.91 Mode :character
## Mean :38.37 Mean : -90.06
## 3rd Qu.:40.91 3rd Qu.: -75.19
## Max. :64.85 Max. : -71.95
## price_range_min price_range_max
## Min. : 0.000 Min. : 7.00
## 1st Qu.: 0.000 1st Qu.:25.00
## Median : 0.000 Median :25.00
## Mean : 4.655 Mean :27.76
## 3rd Qu.: 0.000 3rd Qu.:25.00
## Max. :50.000 Max. :55.00summary(jared)## polla_qid answer votes pollq_id
## Min. : 2.00 Length:375 Min. : 0.000 Min. : 2.00
## 1st Qu.:21.00 Class :character 1st Qu.: 0.000 1st Qu.:21.00
## Median :40.00 Mode :character Median : 2.000 Median :40.00
## Mean :39.93 Mean : 2.832 Mean :39.93
## 3rd Qu.:59.00 3rd Qu.: 4.000 3rd Qu.:59.00
## Max. :77.00 Max. :26.000 Max. :77.00
##
## question place time total_votes
## Length:375 Length:375 Min. :1.344e+09 Min. : 0.00
## Class :character Class :character 1st Qu.:1.395e+09 1st Qu.: 7.00
## Mode :character Mode :character Median :1.467e+09 Median :12.00
## Mean :1.459e+09 Mean :14.16
## 3rd Qu.:1.519e+09 3rd Qu.:19.00
## Max. :1.569e+09 Max. :67.00
##
## percent
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.1667
## Mean :0.2000
## 3rd Qu.:0.3333
## Max. :1.0000
## NA's :5table(barstool$price_level)##
## 0 1 2 3
## 21 216 218 80 and 3 price level have few observations
Data Cleaning
Generally we employ data cleaning steps to derive relevant insights from the data and to get rid of garbage values There are only two missing values in the latitude and longitude column in the Barstool data. Also, there is a value out of range in Dave’s rating.No imputation is being performed as the count is less than 5% of the data in both cases.Critic ratings have 401 zeroes, Community ratings have 41 zeroes, Minimum price range contains 1852 zeroes, Votes have 104 zeroes. Apart from this the data is clean.
Cleaning Datafiniti
Lets create Category column by clubbing similar categories
head(unique_datafiniti)unique_datafiniti$categories <- toupper(unique_datafiniti$categories)
new_datafiniti <- unique_datafiniti %>%
mutate(category = case_when(str_detect(categories,"BAR|BREW|PUB|CLUB|LOUNGE") ~ 'ALCOHOL SERVING', str_detect(categories,"ITAL") ~ 'ITALIAN',str_detect(categories,"CATER") ~ 'CATERERS', TRUE ~ 'NORMAL PIZZA RESTAURANT'))Checking the category column
table(new_datafiniti$category)##
## ALCOHOL SERVING CATERERS ITALIAN
## 63 61 357
## NORMAL PIZZA RESTAURANT
## 1804Lets clean jared data
dim(jared)## [1] 375 9# Removing rows with 0 total votes
jared_rmzero <- jared%>%
filter(total_votes != 0)
#Checking new data
dim(jared_rmzero)## [1] 370 9DT::datatable(barstool)DT::datatable(unique_datafiniti)DT::datatable(jared_rmzero)Converting answer to Numerical Rating
jared_rmzero <- jared_rmzero %>%
mutate(Numerical_Rating = case_when(
answer=="Never Again" ~ 0,
answer=="Poor" ~ 2,
answer=="Fair" ~ 4,
answer=="Average" ~ 6,
answer=="Good"~ 8,
answer=="Excellent" ~ 10))
# Calculating weighted numerical rating
jared_ratings <- jared_rmzero %>%
mutate(Weighted_Rating = Numerical_Rating*votes) %>%
group_by(place) %>%
summarise(Final_Rating = sum(Weighted_Rating)/sum(votes))
# Looking at the final Jared Ratings
head(jared_ratings)EDA
I have applied DataExplorer package as a fast and efficient way to do typical basic EDA. Through the Plot Bar chart of Datafiniti, we found that New York is the dominant state (province) in the dataset.
plot_bar(datafiniti$province)
Correlation between various pizza ratings
barstool_2 <- barstool %>%
rename(
all_score = review_stats_all_average_score,
community_score = review_stats_community_average_score,
critic_score = review_stats_critic_average_score,
dave_score = review_stats_dave_average_score
)
data <- barstool_2 %>% select(provider_rating,community_score,critic_score,dave_score)
data2 <- data[data$critic_score != 0 & data$community_score != 0,]
cor(data2)## provider_rating community_score critic_score dave_score
## provider_rating 1.00000000 0.4339490 0.2017888 0.05343971
## community_score 0.43394896 1.0000000 0.1832775 0.34432172
## critic_score 0.20178876 0.1832775 1.0000000 0.41823049
## dave_score 0.05343971 0.3443217 0.4182305 1.00000000Correlation between critic score and dave score is 0.42
data3 <- data[data$community_score != 0,]
cor(data3)## provider_rating community_score critic_score dave_score
## provider_rating 1.00000000 0.31921901 -0.07935913 0.22096952
## community_score 0.31921901 1.00000000 -0.05570681 0.60522594
## critic_score -0.07935913 -0.05570681 1.00000000 -0.04922468
## dave_score 0.22096952 0.60522594 -0.04922468 1.00000000Correlation between dave score and community score is 0.6, between provider_rating and community score is 0.32 and correlation between provider_rating and dave score is 0.22
Joining Barstool and jared
barstool_jared<- jared_ratings %>%
inner_join(barstool, by = c("place" = "name"))Finding correlation between Jared Final Rating and Barstool All Average Rating
cor(barstool_jared$Final_Rating,barstool_jared$review_stats_all_average_score) ## The correlation is not very high## [1] 0.3026819Comparing pizza ratings in New York with rest of the US
Newyork_Barstool <- barstool[str_detect(barstool$city,"York"),]
Rest_of_US_Barstool <-barstool[!str_detect(barstool$city,"York"),]
mean(Newyork_Barstool$review_stats_all_average_score)## [1] 6.64562mean(Rest_of_US_Barstool$review_stats_all_average_score)## [1] 7.15211mean(Newyork_Barstool$provider_rating)## [1] 3.605159mean(Rest_of_US_Barstool$provider_rating)## [1] 3.748815New York has slightly lower provider and average ratings on average as compared to the rest of US
Comparing pizza ratings across states
table((barstool %>% left_join(new_datafiniti%>% distinct(city,province),by = "city"))$province) ## States except NY have few records##
## CA FL GA IA IL IN KY MI MN NC NV NY OH OK PA SC TX WV
## 8 6 9 3 7 4 7 4 10 1 11 290 10 1 3 4 4 4table1 <- barstool %>% left_join(new_datafiniti%>% distinct(city,province),by = "city") %>% group_by(province) %>% summarise(Avg_provider_rating = mean(provider_rating)) %>% arrange(desc(Avg_provider_rating))
table1 = na.omit(table1)
## Plotting state wise average provider ratings
ggplot(data = table1, aes(x = reorder(province, -Avg_provider_rating), y = Avg_provider_rating)) +
geom_bar(stat = "identity") +
xlab("state") + ylab("Average Provider Rating") +
ggtitle("Statewise Provider rating") +
theme_bw() + geom_text(aes(label=round(Avg_provider_rating,2)), position=position_dodge(width=0.9), vjust=-0.25)+
theme(plot.title = element_text(hjust = 0.5))
table2 <- barstool %>% left_join(new_datafiniti%>% distinct(city,province),by = "city") %>% group_by(province) %>% summarise(Avg_All_Rating = mean(review_stats_all_average_score)) %>% arrange(desc(Avg_All_Rating))
table2 = na.omit(table2)
## Plotting state wise All average ratings
ggplot(data = table2, aes(x = reorder(province, -Avg_All_Rating), y = Avg_All_Rating)) +
geom_bar(stat = "identity",
size=.2) +
xlab("state") + ylab("average all rating") +
ggtitle("statewise avg all average rating") +
theme_bw() + geom_text(aes(label=round(Avg_All_Rating,2)), position=position_dodge(width=0.9), vjust=-0.25)+
theme(plot.title = element_text(hjust = 0.5))
Comparing ratings across categories
Joining Datafinity and Barstool data
datafiniti_barstool<- new_datafiniti %>%
inner_join(barstool, by = "name", "city")
dim(datafiniti_barstool)## [1] 94 32Analysing ratings across pizza categories
boxplot(review_stats_all_average_score~category, data = datafiniti_barstool)
boxplot(provider_rating~category, data = datafiniti_barstool)
Normal Pizza Restaurants have slightly higher All average score as compared to those restaurants which serve Italian pizza, however provider_rating has very similar distribution across both the categories
Comparing price range across pizza categories
new_datafiniti %>% group_by(category) %>% summarise(AVERAGE_MAX_PRICE = mean(price_range_max))new_datafiniti[new_datafiniti$price_range_min != 0,] %>% group_by(category) %>% summarise(AVERAGE_MIN_PRICE = mean(price_range_min))Alcohol serving pizza restaurants have the highest average min and max price range followed by Italian pizza restaurants. Caterers and Normal Pizza restaurants have similar min and max price range.
Do higher priced restaurants have better ratings?
Lets analyze provider ratings
price_low <- barstool[(barstool$price_level == 1) | (barstool$price_level == 0),]
price_high <- barstool[(barstool$price_level == 2) | (barstool$price_level == 3),]
mean_high <- mean(price_high$provider_rating)
mean_high## [1] 3.710177mean_low <- mean(price_low$provider_rating)
mean_low## [1] 3.632911nrow_high <- nrow(price_high)
nrow_high## [1] 226nrow_low <- nrow(price_low)
nrow_low## [1] 237NULL HYPOTHESIS: mean_high - mean_low <= 0 ALTERNATE HYPOTHESIS mean_high - mean_low > 0
se = sqrt(var(price_high$provider_rating)/nrow_high + var(price_low$provider_rating)/nrow_low)
se## [1] 0.04753894Z = (mean_high-mean_low)/se
Z## [1] 1.625312Zalpha = qnorm(0.90)
Zalpha## [1] 1.281552Z > Zalpha We can reject the NULL HYPOTHESIS with 90% confidence. Hence we can say higher priced restaurants have better mean provider_ratings as compared to lower priced restaurants with 90% confidence
Analyzing All Average Score
u1 <- mean(price_high$review_stats_all_average_score)
u1## [1] 7.200656u2 <- mean(price_low$review_stats_all_average_score)
u2## [1] 6.567271NULL HYPOTHESIS: u1 - u2 <= 0 ALTERNATE HYPOTHESIS u1 - u2 > 0
se2 = sqrt(var(price_high$review_stats_all_average_score)/nrow_high + var(price_low$review_stats_all_average_score)/nrow_low)
se2## [1] 0.1288524Z2 = (u1-u2)/se2
Z2## [1] 4.915585Zalpha2 = qnorm(0.99)
Zalpha2## [1] 2.326348Z2 > Zalpha2
We can reject the NULL HYPOTHESIS with 99% confidence. Hence we can say higher priced restaurants have better mean all_average_score as compared to lower priced restaurants with 99% confidence.
Regression for predicting community ratings - OneBite user ratings
# BOXCOX TRANSFORMATION TO GET LAMBDA
boxcox <- MASS::boxcox(community_score ~ dave_score + provider_rating, data = data3)
lambda <- boxcox$x[which.max(boxcox$y)]
lambda## [1] 2data3$community_score2 <- ((data3$community_score ^ lambda) - 1) / lambda
## POLYNOMIAL REGRESSION FOR PREDICTING COMMUNITY RATINGS
fit <- lm(community_score2 ~ poly(dave_score,2) + poly(provider_rating,5), data = data3)Adjusted R square of the model is 0.46. Since we are predicting consumer ratings which has very high variation, we can accept this R square value. Also, p value associated with F test and most of the individual t tests in not significant and we can reject the NULL hypothesis at 95% confidence level.
summary(fit)##
## Call:
## lm(formula = community_score2 ~ poly(dave_score, 2) + poly(provider_rating,
## 5), data = data3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.4405 -2.6747 0.4688 3.3023 23.2799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.3705 0.2885 87.942 < 2e-16 ***
## poly(dave_score, 2)1 93.4312 6.0850 15.354 < 2e-16 ***
## poly(dave_score, 2)2 33.6138 5.9400 5.659 2.85e-08 ***
## poly(provider_rating, 5)1 31.7952 6.0854 5.225 2.77e-07 ***
## poly(provider_rating, 5)2 7.3717 5.9304 1.243 0.2146
## poly(provider_rating, 5)3 -9.4294 5.9264 -1.591 0.1124
## poly(provider_rating, 5)4 12.9945 5.9323 2.190 0.0290 *
## poly(provider_rating, 5)5 -13.2177 5.9295 -2.229 0.0263 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.926 on 414 degrees of freedom
## Multiple R-squared: 0.4633, Adjusted R-squared: 0.4543
## F-statistic: 51.06 on 7 and 414 DF, p-value: < 2.2e-16Regression equation
\[community\_score2 = \frac{community\_score^\lambda-1}{\lambda}\]
\[community\_score2 = 25.3705 + 93.4312*dave\_score + 33.6138*dave\_score^2 + 31.7952*provider\_rating + 7.3717*provider\_rating^2 -9.4294*provider\_rating^3 + 12.9945*provider\_rating^4 - 13.2177*provider\_rating^5\]
Plotting and analyzing the residuals
By performing residual diagnostic, we can see that they satisfy our initial regression assumptions of-
- Normality
- Constant Variance
- Mean 0
plot(fit)
Clustering
We are going to cluster pizza restaurants on the basis of price level and community ratings
#Removing zeroes
barstool_rm <- barstool[barstool$review_stats_community_average_score!=0,]
#Scaling data
barstool_cl <- scale(barstool_rm[c("review_stats_community_average_score","price_level")])
## Creating multiple clusters with different centres
set.seed(5021)
k2 <- kmeans(barstool_cl, centers = 2, nstart = 25)
k3 <- kmeans(barstool_cl, centers = 3, nstart = 25)
k4 <- kmeans(barstool_cl, centers = 4, nstart = 25)
k5 <- kmeans(barstool_cl, centers = 5, nstart = 25)
str(k4)## List of 9
## $ cluster : int [1:422] 4 4 1 1 1 4 2 3 1 1 ...
## $ centers : num [1:4, 1:2] -1.351 0.669 -1.046 0.341 -0.888 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:4] "1" "2" "3" "4"
## .. ..$ : chr [1:2] "review_stats_community_average_score" "price_level"
## $ totss : num 842
## $ withinss : num [1:4] 64.8 40.3 35.6 60.7
## $ tot.withinss: num 202
## $ betweenss : num 640
## $ size : int [1:4] 77 153 46 146
## $ iter : int 3
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"# Visualizing the clusters
p2 <- fviz_cluster(k2, geom = "point", data = barstool_cl) + ggtitle("k = 2")
p3 <- fviz_cluster(k3, geom = "point", data = barstool_cl) + ggtitle("k = 3")
p4 <- fviz_cluster(k4, geom = "point", data = barstool_cl) + ggtitle("k = 4")
p5 <- fviz_cluster(k5, geom = "point", data = barstool_cl) + ggtitle("k = 5")
grid.arrange(p2, p3, p4, p5, nrow = 2)
#Elbow Curve to decide the optimum number of clusters looking at the bend
fviz_nbclust(barstool_cl, kmeans, method = "wss")
# Comparing the clusters
barstool_rm %>%
select("review_stats_community_average_score","price_level") %>%
mutate(Cluster = k4$cluster) %>%
group_by(Cluster) %>%
summarise_all("mean")So finally we have 4 cluster which signify
- Cluster_1 - Low Rating and Low Price restaurants
- Cluster_2 - High Rating and High Price restaurants
- Cluster_3 - Low Rating and High Price restaurants
- Cluster_4 - High Rating and Low Price restaurants
Visualization
#Based on Ratings
barstool_NY <- barstool[barstool$city=='New York',]%>%
na.omit(barstool_NY)
getColor <- function(barstool_NY) {
sapply(barstool_NY$review_stats_all_average_score, function(x) {
if(x <= 4.5) {
"red"
} else if(x <= 6.5) {
"orange"
} else {
"green"
} })
}
icons <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = getColor(barstool_NY)
)
leaflet(barstool_NY) %>%
addTiles() %>%
addAwesomeMarkers(~longitude, ~latitude, icon=icons, label=~as.character(name))%>%
addProviderTiles("CartoDB.Positron") %>%
setView(-73.98, 40.75, zoom = 14) #Based on Clusters
clustered_data <- cbind(k4$cluster,barstool_rm)%>%
na.omit(barstool_rm$latitude) %>%
na.omit(barstool_rm$longitude) %>%
filter(city=="New York")
clustered_data['cluster'] <- clustered_data['k4$cluster']
dim(clustered_data)## [1] 216 24getColor <- function(clustered_data) {
sapply(clustered_data$cluster, function(x) {
if(x == 1) {
"pink"
} else if(x == 2) {
"green"
} else if(x == 3) {
"orange"
}else {
"red"
} })
}
icons <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = getColor(clustered_data)
)
leaflet(clustered_data) %>%
addTiles() %>%
addAwesomeMarkers(~longitude, ~latitude, icon=icons, label=~as.character(name))%>%
addProviderTiles("CartoDB.Positron") %>%
setView(-73.98, 40.75, zoom = 12.5)Cluster_1 PINK - Low Rating and Low Price restaurants Cluster_2 GREEN - High Rating and High Price restaurants Cluster_3 ORANGE - Low Rating and High Price restaurants Cluster_4 RED - High Rating and Low Price restaurants
The above exercise helped us understand various trends in pizza ratings. The following is the summary of the analysis:
We find that there is low correlation between commmunity, provider, critic and jared ratings. Community and Dave ratings have moderately high correlation of ~0.6 New York have lower provider and average pizza ratings on average as compared to rest of the US States with high ratings- IA,OK,FL,OH States with low ratings - WV,MI,NV,SC Restaurants serving Italian Pizza have lower ratings on average as compared to Non Italian Pizza restaurants Alcohol serving and Italian pizza restaurants have higher priced pizza as compared to those that do not fall in this category High priced restaurants have better pizza ratings as compared to low priced restaurants